Logistic Regression from Scratch, with Nesterov Accelerated Descent#34
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stevenkolawole wants to merge 9 commits intoDataScienceNigeria:masterfrom
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Logistic Regression from Scratch, with Nesterov Accelerated Descent#34stevenkolawole wants to merge 9 commits intoDataScienceNigeria:masterfrom
stevenkolawole wants to merge 9 commits intoDataScienceNigeria:masterfrom
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A Customized Logistic Regression with Nesterov Accelerated Gradient with Early Stopping option: Nesterov Accelerated Gradient is theorized to converge by at least, a 10 times faster rate than Stochastic Gradient Descent, and over 25 times faster rate than the naive batch gradient descent. Nesterov Gradient combines the properties of Stochastic Gradient Descent -which supposedly have the properties that allows it to “jump” out of shallow local minima giving it a better chance of finding a true global minimum - with a 'smarter' momentum, that has a somewhat prescient notion of the global minimum, and knows to slow down before the hill slopes up again. But there is a catch; Converging too fast makes it easier for the model to overfit, causing the well-known bias-variance tradeoff. A way to avoid that is to introduce Early Stopping, which works by simply waiting for a certain number of epochs with no improvement in validation loss, and then stopping the iterations.
A Customized Logistic Regression with Nesterov Accelerated Gradient with Early Stopping option: Nesterov Accelerated Gradient is theorized to converge by at least, a 10 times faster rate than Stochastic Gradient Descent, and over 25 times faster rate than the naive batch gradient descent. Nesterov Gradient combines the properties of Stochastic Gradient Descent -which supposedly have the properties that allows it to “jump” out of shallow local minima giving it a better chance of finding a true global minimum- with a 'smarter' momentum, that has a somewhat prescient notion of the global minimum, and knows to slow down before the hill slopes up again. But there is a catch; Converging too fast makes it easier for the model to overfit, causing the well-known bias-variance tradeoff. My way of avoiding that is to introduce Early Stopping, which works by simply waiting for a certain number of epochs with no improvement in validation loss.
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